Online non-affine nonlinear system identification based on state-space neuro-fuzzy models

P. Gil, T. Oliveira, L. Brito Palma

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper proposes a new general recurrent state-space neuro-fuzzy model structure. Three topologies are under assessment, including the state-input recurrent neuro-fuzzy system, the series-parallel recurrent neuro-fuzzy system and the parallel recurrent neuro-fuzzy system. Moreover, the underlying generalised state-space Takagi–Sugeno system is proven to be a universal approximator, and some stability conditions derived for this system. The online training is carried out based on a constrained unscented Kalman filter, where weights, membership functions and consequents are recursively updated. Results from experiments on a benchmark MIMO system demonstrate the applicability and flexibility of the proposed system identification approach.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalSoft Computing
Publication statusAccepted/In press - 13 Jul 2018


  • Kalman filter
  • Neuro-fuzzy systems
  • Nonlinear system identification
  • Takagi–Sugeno models
  • Unscented transform


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